Underutilization with timeout warnings often indicates insufficient parallelism --- meaning there aren't enough executors to process all tasks concurrently.

Solution:

Increase the number of executors to allow more parallel task execution and better resource utilization.

Example configuration:

--conf spark.executor.instances=8

This distributes the workload more effectively across cluster nodes and reduces idle time for pending tasks.

Why the other options are incorrect:

A: Extending timeouts hides the symptom, not the root cause (lack of executors).

B: More memory per executor won't fix scheduling bottlenecks.

C: Reducing partition size may increase overhead and does not fix resource imbalance.

Databricks Exam Guide (June 2025): Section ''Troubleshooting and Tuning Apache Spark DataFrame API Applications'' --- tuning executors and cluster utilization.

Spark Configuration --- executor instances and resource scaling.

===========